The field of the invention is the display of images for human visualization and, particularly, the display of intensity data which has a much higher resolution in contrast levels than is perceivable by humans.
Image display technology is used to transmit visual information to human beings. Common examples are television pictures, photographic film and prints, transparency projection, and computer graphics display monitors. Such images are formed by a series of single, smallest physically resolvable elements, called pixels, in which each pixel has a brightness, or intensity level, which ranges from the blackest-black through mid-grays, to the whitest-white. Especially in the display of medical images, such as ordinary X-ray films, the brightness, or "grey scale", information is as important as the structural, anatomical or morphological information in the image.
In every image display or reception system there will be some lowest level of reliably discernable discrete step in gray-scale value, or contrast resolution. The value of the range from deepest possible "black" to whitest possible "white", divided by the value of smallest discernable step in that intensity is called the image's dynamic range. The dynamic range of image pixel values commonly is expressed logarithmically by using logarithms of base-2. ##EQU1## This is technically convenient because it corresponds to the binary counting scheme employed to store data in digital computers. For example, if the dynamic range of an image is 12 bits, then the brightness value for each pixel in the image will require 12 bits of memory for storage.
Image data comes from a primary modality instrument such as a television camera, an X-ray machine, an ultrasound system, or a magnetic resonance imaging (MRI) system. The intensity values in the acquired data array have a one-to-one correspondence with the pixels in the final image to be displayed. The numerical intensity value stored in each location of the data array is employed to control the brightness of its corresponding display pixel.
But the originally acquired data array values usually cannot be directly transferred to the display. They do not match appropriately to the display medium, or the visual needs of the observer. For example, MRI acquired data arrays may have values that are negative algebraically, as well as positive, and one cannot generate negative light intensity for a display. Consequently, a numerical scaling process must be used to offset the baseline level to fit the range of values in the acquired data array onto the range of values which can be displayed physically. Such "static windowing", or "contrast windowing" is a well known procedure.
While the offset image array values may in principle be physically displayable, further processing may be required to meet the needs of human observers. For example, while 12-bit intensity, or brightness, values may be presented on a CRT display, humans are not able to perceive the very small changes in brightness that such data presents. Indeed, humans have a dynamic brightness range under the best of conditions in the range of 6 to 7 bits with the result that the least significant 5 or 6 bits of the 12-bit display brightness information is not perceived. Thus, while the least significant bits of the 12-bit image array data may indicate meaningful variations of 5 or 6 bits in brightness dynamic range throughout a region of the image, the human observer may only perceive a single brightness, or shade. The loss of such information can have an enormous impact in medical applications where such detailed variations in brightness may represent important anatomical or morphological features.
One method for overcoming this problem begins by placing the average of the intensity values at the midpoint of the 6 to 8-bit useful dynamic range of the display. This method is referred to as "static contrast windowing" and it requires prior knowledge of the average value of the region of interest in the image. Such static contrast windowing is routinely performed by radiologists who adjust the X-ray exposure factors and film-screen speed to put the average X-ray transmission through the anatomical region of interest at the central sensitivity region of the film.
Such static contrast windowing is illustrated in FIG. 1 where the horizontal axis represents the entire range of intensity values which the image data may have, and the vertical axis indicates the more limited range of brightnesses on the visual display. The dashed lines 1 and 2 define the full range, or "contrast window" of intensity values which will be mapped to the display. The solid line 3 is a transfer curve which indicates how the intensity values are mapped to the display brightness values. Intensity values which are below the contrast window in value are limited to black and values above the contrast window are "clipped" and displayed white. An intensity value within the contrast window is mapped to a corresponding display brightness value as indicated by the arrows 4. The display value is determined by the shape of the transfer curve 3, which in FIG. 1 is a straight line.
Contrast windowing is "static" when the window and the transfer curve are fixed for the conversion of the entire image data array. Details within the intensity range of the contrast window are shown on the display within a 6 to 8 bit dynamic range. The remaining parts of the image, however, are shown either "too white" or "too black" and details therein are not perceivable. For diagnostic medical image studies, all regions of brightness may require examination for abnormal anatomic details. This requires that multiple images be displayed, each with a different contrast window which insures that anatomical details at all brightness levels will be displayed. Modern digital imaging instruments such as X-ray CT, digital-subtraction angiography, digital cassette radiography and nuclear magnetic resonance imaging work stations have controls for setting the LEVEL, or midpoint, of the brightness window and its WIDTH.
One approach commonly used in X-ray CT practice is to employ a transfer curve that has two contrast windows. Such a dual window approach is illustrated in FIG. 2, where one window 5 is set to encompass the brightness levels around bone and the other window 6 is set to encompass the brightness levels around soft tissue. This works because the anatomy is regular, and one knows a-priori what part is soft tissue and what part is bone. It is not overwhelmingly confusing to have two regions displayed in the same shifted gray-scale in the same display. However, in any anatomic regions that are intermediate in brightness between the windowed soft tissue and bone, the X-ray CT image values become hopelessly muddled.
Another more recent technique for mapping data brightness levels to a display having a limited dynamic range is known in the art as adaptive contrast enhancement, for which the most successful variant is "adaptive histogram equalization" (AHE). Unlike prior techniques which are "static", the AHE technique does not employ a fixed contrast window for the entire image. Instead, the AHE technique looks at each datum intensity value in the acquired data array one at a time and compares it with the values in a local surrounding spatial area, or "context region". The length and width of the context region may, for example, range from one sixth to one sixtieth of the length and width of the entire image data array. While there are many variations on the precise calculations employed with this technique, the general idea is to map the centered datum value to a display brightness which provides good contrast with respect to the other data values generally within the same context region. The calculations are performed, in principle, at each pixel location in the image data array with respect to its surrounding context region and the technique is, therefore, computationally intense. The AHE technique and some of its variations are described in "The Effectiveness of Adaptive Contrast Enhancement (in Medical Images)", Zimmerman, J. B, Ph.D. Thesis, 1985, UNC, Chapel Hill, University of Microfilms International, Ann Arbor, Mich.; "Spatially Variant Contrast Enhancement Using Local Range Modification", Fahnestock, J. D. and Schowengerdt, R. A., Optical Engineering, Vol. 22(3):378-381 (1983); and "Algorithms For Adaptive Histogram Equalization", Pizer, S. M., Austin, J. D. et al., SPIE Vol. 671, Physics and Engineering of Computer Multidimensional Imaging and Processing (1986).